Rethinking remdesivir for COVID-19: A Bayesian reanalysis of trial findings.

• Main Authors: Joyce M Hoek, Sarahanne M Field, Ymkje Anna de Vries, Maximilian Linde, Merle-Marie Pittelkow, Jasmine Muradchanian, Don van Ravenzwaaij
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2021-01-01
• Series: PLoS ONE
• Online Access: https://doi.org/10.1371/journal.pone.0255093

<h4>Background</h4>Following testing in clinical trials, the use of remdesivir for treatment of COVID-19 has been authorized for use in parts of the world, including the USA and Europe. Early authorizations were largely based on results from two clinical trials. A third study published by Wang et al. was underpowered and deemed inconclusive. Although regulators have shown an interest in interpreting the Wang et al. study, under a frequentist framework it is difficult to determine if the non-significant finding was caused by a lack of power or by the absence of an effect. Bayesian hypothesis testing does allow for quantification of evidence in favor of the absence of an effect.<h4>Findings</h4>Results of our Bayesian reanalysis of the three trials show ambiguous evidence for the primary outcome of clinical improvement and moderate evidence against the secondary outcome of decreased mortality rate. Additional analyses of three studies published after initial marketing approval support these findings.<h4>Conclusions</h4>We recommend that regulatory bodies take all available evidence into account for endorsement decisions. A Bayesian approach can be beneficial, in particular in case of statistically non-significant results. This is especially pressing when limited clinical efficacy data is available.